Vehicle classification is a crucial task owing to vehicles' diverse and intricate features, such as edges, colors, shadows, corners, and textures. The accurate classification of vehicles enables their detection and identification on roads and facilitates the development of an electronic tollcollection system for smart cities. Furthermore, vehicle classification is useful for traffic signal control strategy. However, achieving accurate vehicle classification poses significant challenges due to the limited processing time for real-time applications, image resolution, illumination variations in the video, and other interferences. This study proposes a method for automated automobile detection, recognition, and classification using statistics derived from approximately 11,000 images. We employ SURF-based detection and different classifiers to categorize vehicles into three groups. The Traffic Management System (TMS) is crucial for studying mobility and smart cities. Our study achieves a high automobile classification rate of 91% with the medium Gaussian Support Vector Machine (SVM) classifier. The paper's main objective is to analyze five object classifiers for vehicle recognition: Decision Tree, Discriminant Analysis, SVM, K-Nearest Neighbor Classifier (KNN), and Ensemble Classifier. In the discussion section, we present the limitations of our work and provide insights into future research directions.

Using limited materials for numerical modeling of structural samples and comparing the behavior of samples with each other in order to reduce the number of straw test samples in order to reduce laboratory costs is one of the best methods today. ANSYS is one of the finite element software that is used for modeling reinforced concrete elements by Solid65 element. As a case study, a concrete-filled steel column under cyclic loading is examined and the best value for the Crushed Stiffness Factor (CSTIF) parameter that is one of Solid65 element parameters is suggested to confirm the modeling. By investigating the results, it can be concluded that this parameter is one of the most important factors that plays a significant role in convergence in ANSYS software for modeling CFT columns.

The aim of this study was to examine the effect of different fertilizers on the yield and antioxidant capacity of two lettuce genotypes “Santoro RZ” and “Kiribati RZ”.

Lettuce genotypes are fertilized with organic fertilizer (Slavol) and organic-inorganic NPK fertilizer (Fitofert hemisuper plus ) during the vegetation. The analyzed parameters were root length and head weight of lettuce, total phenols, and flavonoids, as well as antioxidant capacity. Lettuce genotypes “Santoro RZ” and “Kiribati RZ” fertilized with organic fertilization showed the highest content of total phenols (358.13 ± 1.30 mg RU/100 g of fresh sample), the total content of flavonoids (114.22 ± 0.3 mg RE/100 g of fresh sample) and antioxidant capacity (neutralization of DPHH radicals 58.72 ± 1.88%).

The results revealed that the yield and antioxidant capacity of lettuce can be improved by using organic fertilizers.